New imaging modalities to distinguish rare uterine mesenchymal cancers from benign uterine lesions Review


Authors: Causa Andrieu, P.; Woo, S.; Kim, T. H.; Kertowidjojo, E.; Hodgson, A.; Sun, S.
Review Title: New imaging modalities to distinguish rare uterine mesenchymal cancers from benign uterine lesions
Abstract: PURPOSE OF REVIEW: Uterine sarcomas are rare and are often challenging to differentiate on imaging from benign mimics, such as leiomyoma. As functional MRI techniques have improved and new adjuncts, such as machine learning and texture analysis, are now being investigated, it is helpful to be aware of the current literature on imaging features that may sometimes allow for preoperative distinction. RECENT FINDINGS: MRI, with both conventional and functional imaging, is the modality of choice for evaluating uterine mesenchymal tumors, especially in differentiating uterine leiomyosarcoma from leiomyoma through validated diagnostic algorithms. MRI is sometimes helpful in differentiating high-grade stromal sarcoma from low-grade stromal sarcoma or differentiating endometrial stromal sarcoma from endometrial carcinoma. However, imaging remains nonspecific for evaluating rarer neoplasms, such as uterine tumor resembling ovarian sex cord tumor or perivascular epithelioid cell tumor, primarily because of the small number and power of relevant studies. SUMMARY: Through advances in MRI techniques and novel investigational imaging adjuncts, such as machine learning and texture analysis, imaging differentiation of malignant from benign uterine mesenchymal tumors has improved and could help reduce morbidity relating to misdiagnosis or diagnostic delays. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
Journal Title: Current Opinion in Oncology
Volume: 33
Issue: 5
ISSN: 1040-8746
Publisher: Lippincott Williams & Wilkins  
Date Published: 2021-09-01
Start Page: 464
End Page: 475
Language: English
DOI: 10.1097/cco.0000000000000758
PUBMED: 34172593
PROVIDER: scopus
PMCID: PMC8376762
DOI/URL:
Notes: Article -- Export Date: 1 October 2021 -- Source: Scopus
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  1. Sungmin Woo
    62 Woo
  2. Anjelica Jane Hodgson
    18 Hodgson